code: def _drop_col(X, index):
      """Drop index columns from numpy array or sparse matrix"""
      cols = np.arange(X.shape[1])
      cols_to_keep = np.where(np.logical_not(np.in1d(cols, index)))[0]
      return X[:, cols_to_keep]
  
imports: import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin, is_classifier
  from sklearn.utils.metaestimators import if_delegate_has_method
  from sklearn.utils import check_X_y, safe_sqr
  from sklearn.model_selection import check_cv
  from sklearn.metrics import check_scoring
  from joblib import Parallel, delayed
  from itertools import product
  from scipy.sparse import issparse
  from .validation import _check_is_fitted
  from .base import _parse_partitions, _clone
  from .utils import _safe_split
label_desc: Use image node_id 1 for calling a global function with example usage: _drop_col(X, index) and returns: X